scholarly journals Clinical information extraction for preterm birth risk prediction

2020 ◽  
Vol 110 ◽  
pp. 103544
Author(s):  
Lucas Sterckx ◽  
Gilles Vandewiele ◽  
Isabelle Dehaene ◽  
Olivier Janssens ◽  
Femke Ongenae ◽  
...  
2021 ◽  
pp. 103844
Author(s):  
Julia Wu ◽  
Venkatesh Sivaraman ◽  
Dheekshita Kumar ◽  
Juan M. Banda ◽  
David Sontag

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
William Scuba ◽  
Melissa Tharp ◽  
Danielle Mowery ◽  
Eugene Tseytlin ◽  
Yang Liu ◽  
...  

Author(s):  
Shany Biton ◽  
Sheina Gendelman ◽  
Antônio H Ribeiro ◽  
Gabriela Miana ◽  
Carla Moreira ◽  
...  

Abstract Aims This study aims to assess whether information derived from the raw 12-lead electrocardiogram (ECG) combined with clinical information is predictive of atrial fibrillation (AF) development. Methods We use a subset of the Telehealth Network of Minas Gerais (TNMG) database consisting of patients that had repeated 12-lead ECG measurements between 2010-2017 that is 1,130,404 recordings from 415,389 unique patients. Median and interquartile of age for the recordings were 58 (46-69) and 38% of the patients were males. Recordings were assigned to train-validation and test sets in an 80:20% split which was stratified by class, age and gender. A random forest classifier was trained to predict, for a given recording, the risk of AF development within 5-years. We use features obtained from different modalities, namely demographics, clinical information, engineered features, and features from deep representation learning. Results The best model performance on the test set was obtained for the model combining features from all modalities with an AUROC=0.909 against the best single modality model which had an AUROC=0.839. Conclusion Our study has important clinical implications for AF management. It is the first study integrating feature engineering, deep learning and EMR metadata to create a risk prediction tool for the management of patients at risk of AF. The best model that includes features from all modalities demonstrates that human knowledge in electrophysiology combined with deep learning outperforms any single modality approach. The high performance obtained suggest that structural changes in the 12-lead ECG are associated with existing or impending AF.


2011 ◽  
Vol 18 (5) ◽  
pp. 557-562 ◽  
Author(s):  
Berry de Bruijn ◽  
Colin Cherry ◽  
Svetlana Kiritchenko ◽  
Joel Martin ◽  
Xiaodan Zhu

PLoS Medicine ◽  
2021 ◽  
Vol 18 (7) ◽  
pp. e1003686
Author(s):  
Sarah J. Stock ◽  
Margaret Horne ◽  
Merel Bruijn ◽  
Helen White ◽  
Kathleen A. Boyd ◽  
...  

Background Timely interventions in women presenting with preterm labour can substantially improve health outcomes for preterm babies. However, establishing such a diagnosis is very challenging, as signs and symptoms of preterm labour are common and can be nonspecific. We aimed to develop and externally validate a risk prediction model using concentration of vaginal fluid fetal fibronectin (quantitative fFN), in combination with clinical risk factors, for the prediction of spontaneous preterm birth and assessed its cost-effectiveness. Methods and findings Pregnant women included in the analyses were 22+0 to 34+6 weeks gestation with signs and symptoms of preterm labour. The primary outcome was spontaneous preterm birth within 7 days of quantitative fFN test. The risk prediction model was developed and internally validated in an individual participant data (IPD) meta-analysis of 5 European prospective cohort studies (2009 to 2016; 1,783 women; mean age 29.7 years; median BMI 24.8 kg/m2; 67.6% White; 11.7% smokers; 51.8% nulliparous; 10.4% with multiple pregnancy; 139 [7.8%] with spontaneous preterm birth within 7 days). The model was then externally validated in a prospective cohort study in 26 United Kingdom centres (2016 to 2018; 2,924 women; mean age 28.2 years; median BMI 25.4 kg/m2; 88.2% White; 21% smokers; 35.2% nulliparous; 3.5% with multiple pregnancy; 85 [2.9%] with spontaneous preterm birth within 7 days). The developed risk prediction model for spontaneous preterm birth within 7 days included quantitative fFN, current smoking, not White ethnicity, nulliparity, and multiple pregnancy. After internal validation, the optimism adjusted area under the curve was 0.89 (95% CI 0.86 to 0.92), and the optimism adjusted Nagelkerke R2 was 35% (95% CI 33% to 37%). On external validation in the prospective UK cohort population, the area under the curve was 0.89 (95% CI 0.84 to 0.94), and Nagelkerke R2 of 36% (95% CI: 34% to 38%). Recalibration of the model’s intercept was required to ensure overall calibration-in-the-large. A calibration curve suggested close agreement between predicted and observed risks in the range of predictions 0% to 10%, but some miscalibration (underprediction) at higher risks (slope 1.24 (95% CI 1.23 to 1.26)). Despite any miscalibration, the net benefit of the model was higher than “treat all” or “treat none” strategies for thresholds up to about 15% risk. The economic analysis found the prognostic model was cost effective, compared to using qualitative fFN, at a threshold for hospital admission and treatment of ≥2% risk of preterm birth within 7 days. Study limitations include the limited number of participants who are not White and levels of missing data for certain variables in the development dataset. Conclusions In this study, we found that a risk prediction model including vaginal fFN concentration and clinical risk factors showed promising performance in the prediction of spontaneous preterm birth within 7 days of test and has potential to inform management decisions for women with threatened preterm labour. Further evaluation of the risk prediction model in clinical practice is required to determine whether the risk prediction model improves clinical outcomes if used in practice. Trial registration The study was approved by the West of Scotland Research Ethics Committee (16/WS/0068). The study was registered with ISRCTN Registry (ISRCTN 41598423) and NIHR Portfolio (CPMS: 31277).


2020 ◽  
Vol 21 (15) ◽  
pp. 5283
Author(s):  
Ana Correia-Branco ◽  
Monica P. Rincon ◽  
Leonardo M. Pereira ◽  
Mary C. Wallingford

Inorganic phosphate (Pi) is an essential nutrient that fulfills critical roles in human health. It enables skeletal ossification, supports cellular structure and organelle function, and serves key biochemical roles in energetics and molecular signaling. Pi homeostasis is modulated through diet, intestinal uptake, renal reabsorption, and mobilization of stores in bone and extracellular compartments. Disrupted Pi homeostasis is associated with phosphate wasting, mineral and bone disorders, and vascular calcification. Mechanisms of Pi homeostasis in pregnancy remain incompletely understood. The study presented herein examined biological fluid Pi characteristics over the course of gestation. Correlations with gestation age, pregnancy number, preterm birth, preeclampsia, diabetes mellitus, and placental calcification were evaluated during the last trimester. The results support that maternal urinary Pi levels increased during the third trimester of pregnancy. Reduced levels were observed with previous pregnancy. Amniotic fluid Pi levels decreased with gestation while low second trimester levels associated with preterm birth. No significant difference in urinary Pi levels was observed between preeclampsia and controls (8.50 ± 2.74 vs. 11.52 ± 2.90 mmol/L). Moreover, increased maternal urinary Pi was associated with preexisting diabetes mellitus in preeclampsia. Potential confounding factors in this study are maternal age at delivery and body mass index (BMI)—information which we do not have access to for this cohort. In conclusion, Pi levels provide clinical information regarding the pathogenesis of pregnancy-related complications, supporting that phosphate should be examined more closely and in larger populations.


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